69 gaussian-process-regression Postdoctoral positions at Oak Ridge National Laboratory
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Requisition Id 16712 Overview: We are seeking a Postdoctoral Research Associate in inorganic or materials chemistry/science or chemical engineer who will focus on separation, processing, and
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Gaussian-process emulators for accelerating parameter estimation and uncertainty propagation Selective cross-scale evaluation using complementary ecosystem observations (e.g., experiments) to test how AI
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in delivering the intended residential energy cost and other household direct benefits. Data collection, processing/curation, and analysis. Development and implementation of econometric analysis (e.g
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such as classifier free guided diffusion models, transformers with multi-headed attention, physics-informed neural networks, materials foundational models with multi-task learning, symbolic regression
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one
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models of gas transport and process behavior in industrial systems Collaborate with a team of scientists from across the national laboratory complex on modeling efforts Extend process flow modeling across
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component (ELM) and its peatland-specific configuration (ELM-Peatlands), with a focus on improving the representation of nutrient cycling dynamics and their coupling to hydrological and ecosystem processes
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component (ELM) and its peatland-specific configuration (ELM-Peatlands), with a focus on improving the representation of nutrient cycling dynamics and their coupling to hydrological and ecosystem processes
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advanced manufacturing processes. This position resides in the Deposition Science and Technology Group in the Manufacturing Science Division (MSD), Energy Science and Technology Directorate (ESTD) at Oak
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mechanical testing to uncover processing–microstructure–property relationships. The candidate will have opportunities to interact with multidisciplinary teams and contribute to high-impact publications and